Dynamic generation of a quantization matrix for compression of a digital object

ABSTRACT

Some aspects of the disclosure relate to a compression technique that can permit determining dynamically a satisfactory quantization matrix based at least on properties intrinsic to a digital object being compressed and a predetermined compression quality criterion, wherein the quantization matrix is associated with a specific space-domain-to-frequency-domain transforms. In one aspect, the compression technique can permit creation of a compressed digital object that can satisfy a predetermined a compression quality criterion.

BACKGROUND

Methodologies for processing digital content (pictures, video, audio,etc.) generally entail application of mathematical formalisms suitablefor discrete data objects. Some of those formalisms are devised forcompression of a discrete data object (also referred to as a digitalobject), wherein compression generally refers to generation of arepresentation of the discrete data object that consumes less memoryresources than an original representation of such object. As part ofcompression of a digital object, quantization of a frequency-domainrepresentation of a data structure representing the digital object isapplied. While quantization permits, at least in part, compression ofthe digital object, such compression generally is lossy. Thus, qualityof a restored digital object can be affected by specific configurationof quantization.

SUMMARY

It is to be understood that this summary is not an extensive overview ofthe disclosure. This summary is exemplary and not restrictive, and it isintended to neither identify key or critical elements of the disclosurenor delineate the scope thereof. The sole purpose of this summary is toexplain and exemplify certain concepts of the disclosure as anintroduction to the following complete and extensive detaileddescription.

The disclosure relates, in one aspect, to a compression technique thatcan permit determining dynamically a satisfactory (e.g., optimal)quantization matrix based at least on properties intrinsic to a digitalobject being compressed and a predetermined compression qualitycriterion, wherein the quantization matrix is associated with specificspace-domain-to-frequency-domain transforms. Thus, in one aspect, thecompression technique can permit creation of a compressed digital objectthat can satisfy a predetermined compression quality criterion. In oneimplementation, determination of the quantization matrix can beaccomplished through evaluation of a functional relationship between adigital variation metric representative of such properties and aparameter indicative of the quantization matrix. Certain embodiments ofthe disclosure permit generation of the functional relationship based onanalysis of compression features of a plurality of digital objects, thefeatures comprising digital variation metrics of original digitalobjects and correlation metrics between the original objects andrespective restored instances of such objects. In anotherimplementation, the quantization matrix can be determined according to alook-up table having the digital variation metric and/or a correlationmetric as primary keys, wherein the correlation metrics are indicativeof statistical relationship between an original digital object and arestored instance of such object obtained after compression of theoriginal digital object. Various features of the disclosure areillustrated with quantization matrices associated with discrete cosinetransforms (DCT), but it should be appreciated that the disclosure canapply to other quantization matrices associated with otherspace-domain-to-frequency-domain transforms, such as wavelet transforms.

Some embodiments of the disclosure provide various advantages whencompared to conventional technologies for compression of digitalobjects. For example, some embodiments can provide, dynamically, aquantization matrix suitable to achieve a specific compression qualityfor compression of a digital object.

Additional aspects or advantages of the subject disclosure will be setforth in part in the description which follows, and in part will beobvious from the description, or may be learned by practice of thesubject disclosure. The advantages of the subject disclosure will berealized and attained by means of the elements and combinationsparticularly pointed out in the appended claims. It is to be understoodthat both the foregoing general description and the following detaileddescription are exemplary and explanatory only and are not restrictiveof the subject disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The annexed drawings are an integral part of the subject disclosure andillustrate exemplary embodiments thereof. Together with the descriptionset forth herein and the claims appended hereto, the annexed drawingsserve to explain various principles, features, or aspects of thedisclosure.

FIG. 1 illustrates an exemplary compression technique in accordance withone or more aspects of the disclosure.

FIG. 2 illustrates an exemplary system for compression of a digitalobject in accordance with one or more aspects of the disclosure.

FIG. 3 illustrates an exemplary compression technique in accordance withone or more aspects of the disclosure.

FIG. 4 illustrates an exemplary rendering of a quantization matrix indexas a function of a digital variation metric in accordance with one ormore aspects of the disclosure.

FIG. 5 illustrates results of the application of a linear regression tothe exemplary data rendered in FIG. 4 in accordance with one or moreaspects of the disclosure.

FIGS. 6A-6B illustrates an exemplary computing device in accordance withone or more aspects of the disclosure.

FIGS. 7-9 illustrate exemplary methods in accordance with one or moreaspects of the disclosure.

DETAILED DESCRIPTION

The various aspects described herein can be understood more readily byreference to the following detailed description of exemplary embodimentsof the subject disclosure and to the annexed drawings and their previousand following description.

Before the present systems, articles, apparatuses, and methods aredisclosed and described, it is to be understood that the subjectdisclosure is not limited to specific systems, articles, apparatuses,and methods for dynamic compression of digital content (e.g., non-motionpictures, motion pictures or video segments, audio segments, and thelike) in an active replication topology of a distributed contentrepository. It is also to be understood that the terminology employedherein is for the purpose of describing particular, non-exclusiveembodiments only and is not intended to be limiting.

As used in the specification and the appended claims, the singular forms“a,” “an” and “the” include plural referents unless the context clearlydictates otherwise. Ranges may be expressed herein as from “about” oneparticular value, and/or to “about” another particular value. When sucha range is expressed, another embodiment includes from the oneparticular value and/or to the other particular value. Similarly, whenvalues are expressed as approximations, by use of the antecedent“about,” it will be understood that the particular value forms anotherembodiment. It will be further understood that the endpoints of each ofthe ranges are significant both in relation to the other endpoint, andindependently of the other endpoint.

As utilized in this specification and the annexed drawings, the terms“system,” “engine,” “component,” “unit,” “interface,” “platform,”“node,” “function” and the like are intended to include acomputer-related entity or an entity related to an operational apparatuswith one or more specific functionalities, wherein the computer-relatedentity or the entity related to the operational apparatus can be eitherhardware, a combination of hardware and software, software, or softwarein execution. Such entities also are referred to as “functionalelements.” As an example, a unit can be, but is not limited to being, aprocess running on a processor, a processor, an object (metadata object,data object, signaling object), an executable computer program, a threadof execution, a program, a memory (e.g., a hard-disc drive), and/or acomputer. As another example, a unit can be an apparatus with specificfunctionality provided by mechanical parts operated by electric orelectronic circuitry which is operated by a software application or afirmware application executed by a processor, wherein the processor canbe internal or external to the apparatus and can execute at least aportion of the software application or the firmware application. As yetanother example, a unit can be an apparatus that provides specificfunctionality through electronic functional elements without mechanicalparts, the electronic functional elements can include a processortherein to execute software or firmware that provides, at least in part,the functionality of the electronic functional elements. The foregoingexamples and related illustrations are but a few examples and are notintended to be limiting. In addition, while such illustrations arepresented for a unit, the foregoing examples also apply to a system, anengine, a node, an interface, a function, a component, a platform, andthe like. It is noted that in certain embodiments, or in connection withcertain aspects or features of such embodiments, the terms “system,”“layer,” “unit,” “component,” “interface,” “platform” “node,” “function”can be utilized interchangeably.

Throughout the description and claims of this specification, the words“comprise,” “include,” and “having” and their variations, such as“comprising” and “comprises,” “include” and “including,” “having” and“has,” mean “including but not limited to,” and are not intended toexclude, for example, other units, nodes, components, functions,interfaces, actions, steps, or the like. “Exemplary” means “an exampleof” and is not intended to convey an indication of a preferred or idealembodiment. “Such as” is not used in a restrictive sense, but forexplanatory purposes.

Disclosed are components that can be utilized to perform the disclosedmethods, devices, and/or systems. These and other components aredisclosed herein, and it is understood that when combinations, subsets,interactions, groups, etc. of these components are disclosed that whilespecific reference of each various individual and collectivecombinations and permutation(s) of these may not be explicitlydisclosed, each is specifically contemplated and described herein, forall methods, devices, and/or systems. This applies to all aspects of thesubject disclosure including steps, or actions, in the disclosedmethod(s). Thus, if there are a variety of additional steps, or actions,that can be performed, then it is understood that each of suchadditional steps, or actions, can be performed with any specificembodiment or combination of embodiments of the disclosed methods.

As it will be readily appreciated, in one aspect, the methods, devices,and/or systems of the disclosure can take the form of an entirelyhardware embodiment, an entirely software embodiment, or an embodimentcombining software and hardware aspects. In an additional or alternativeaspect, the methods, devices, and/or systems can take the form of acomputer program product on a computer-readable storage medium havingcomputer-readable program instructions (e.g., computer software)embodied in the storage medium. More particularly, the disclosedmethods, devices, and/or systems can take the form of web-implementedcomputer software. Any suitable computer-readable storage medium can beutilized including hard disks, CD-ROMs, optical storage devices, ormagnetic storage devices.

Embodiments of the methods and systems are described below withreference to block diagrams and flowchart and/or call-flow illustrationsof methods, systems, apparatuses and computer program products. It willbe understood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, respectively, can be implemented by computerprogram instructions. Such computer program instructions may be loadedonto a general purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions which execute on the computer or other programmabledata processing apparatus create a means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including computer-readableinstructions for implementing the function specified in the flowchartblock or blocks. The computer program instructions also can be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps, or acts, to be performed on the computeror other programmable apparatus to produce a computer-implementedprocess such that the instructions that execute on the computer or otherprogrammable apparatus provide steps for implementing the functionsspecified in the flowchart block or blocks.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of means for performing the specified functions,combinations of steps for performing the specified functions and programinstruction means for performing the specified functions. It will alsobe understood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, can be implemented by special purposehardware-based computer systems that can perform the specified functionsor steps, or combinations of special purpose hardware and computerinstructions.

Reference will now be made in detail to the various embodiments andrelated aspects of the subject disclosure, examples of which areillustrated in the accompanying drawings and their previous andfollowing description. Wherever possible, the same reference numbers areused throughout the drawings to refer to the same or like parts.

The disclosure identifies and addresses, in one aspect, the issue ofheuristic determination of quantization matrices for image processing,such as compression. One shortcoming of conventional approaches is thatquantization matrices are not customized to specific images, thus imagequality after restoration of two or more images that have beencompressed utilizing a conventional quantization matrix associated witha specific quality index typically is not consistent between suchimages.

In processing of digital content, Joint Photographic Experts Group(JPEG) image compression and variants thereof can contribute to reducethe size of (or to compress) data structures representing the digitalimages, thus permitting such data structures, and the related digitalcontent, to be easily stored and transmitted. Accordingly, the JPEGimage compression technique is now an integral part of most image andvideo compression standards, such as Moving Picture Experts Group-2(MPEG-2) or H.264. In a scenario in which a non-moving picture (orimage) is compressed, a direct cosine transform—a lossy compressionscheme—can be applied to convert the non-moving image from arepresentation in spatial domain to frequency domain.

In a quantization stage of compression, frequency components that aremost perceptible to the human eye can be retained, whereas otherfrequency components that are perceived less can be removed from arepresentation of the digital content. Such elimination is at the coreof the JPEG compression technique because a majority of frequencycomponents can be removed, therefore permitting straightforwardapplication of compression techniques on the remaining data and therebyachieve substantive compression rates (e.g., nearly 90% compressionrate).

In one aspect, quality of a JPEG compressed image is largely a functionof the number of low and high frequency components that can be retainedin a data structure that embodies a compressed digital object. It shouldbe appreciated that it generally is desirable to retain lower frequencycomponents because the human eye can be more sensitive to suchcomponents and absence thereof can be readily perceived, with theensuing lowering of perceived quality of a rendering of the compresseddigital object. Yet, retention of larger number of frequencies,including high frequencies, in such data structure can permitrestoration of the compressed digital object with higher fidelity. Thus,image quality after compression can be a function of the number ofhigher frequency components that can be retained in an image compressionscheme. In one aspect, as described herein, a larger count of higherfrequency components generally yields greater quality. Certainconventional JPEG image compression techniques can permit a user deviceto specify a quality index for a compressed digital object (e.g., a datastructure representative of a compressed image), wherein the qualityindex can range from 1 to 100, with higher indices being representativeof higher quality. In one aspect, the quality index can be mapped to acorresponding DCT quantization matrix, which can determine, at least inpart, a set of one or more candidate frequency components to include inthe compressed digital object. Such a mapping from quality index to DCTquantization matrix can be obtained, for example, from experiments onthe human visual system. As described herein, a shortcoming of suchcompression approach is that for a specific quality index (or qualitylevel), the actual quality of the compressed image is not uniform acrossmultiple images. For example, a compression scheme implemented with aquality index of 90 on two different original digital images cangenerate respective compressed digital images yielding different degreesof correlation between respective restored digital images and therespective original digital images. It should be appreciated that theterm “correlation” as utilized in the subject disclosure refers to ameasure of correlation among two sets of static data rather than toauto-correlation or cross-correlation among time-dependent signals ascommonly applied in digital signal processing.

As an illustration, a conventional method for determining a discretecosine transform (DCT) quantization matrix for a specific quality indextypically is nearly exclusively based on subjective experimentationinvolving the human visual system. Such quality indices generally rangefrom 1 to 100, with 1 being representative of low quality. As anexample, Eq. (1) illustrates a conventional quantization matrix Q₀ for8×8 macroblocks (commonly referred to as Minimum Coded Unit) andassociated with a quality index of 50 (such index referred to as Q50):

$\begin{matrix}{{\overset{\leftrightarrow}{Q}}_{0} = {\begin{bmatrix}16 & 11 & 10 & 16 & 24 & 40 & 51 & 61 \\12 & 12 & 14 & 19 & 26 & 58 & 60 & 55 \\14 & 13 & 16 & 24 & 40 & 57 & 69 & 56 \\14 & 17 & 22 & 29 & 51 & 87 & 80 & 62 \\18 & 22 & 37 & 56 & 68 & 109 & 103 & 77 \\24 & 35 & 55 & 64 & 81 & 104 & 113 & 92 \\49 & 64 & 78 & 87 & 103 & 121 & 120 & 101 \\72 & 92 & 95 & 98 & 112 & 100 & 103 & 99\end{bmatrix}.}} & {{Eq}.\mspace{14mu} (1)}\end{matrix}$

Quantization matrices for 8×8 macroblocks and a quality index q greaterthan 50 can be derived by multiplying Q₀ by η=(100−q)/50. Quantizationmatrices for a quality index q′ lower than 50 can be derived bymultiplying Q₀ by (50/q′). It can be appreciated that, conventionally,changes to a quantization are not directly related to specificfeature(s) of a digital object to be compressed.

In addition to quantization, compression techniques can comprise one ormore types of encoding, e.g., entropy encoding, run-length-encoding,Huffman encoding, or the like. Encoding can further the compressionaccomplished via quantization.

As described in greater detail below, the disclosure relates, in oneaspect, to a compression technique (system(s), method(s), system(s) andmethod(s), etc.) that can permit determining dynamically a satisfactory(e.g., optimal or nearly-optimal) quantization matrix associated with aspace-domain-to-frequency-domain transform (e.g., DCT, wavelettransform, or the like) based at least on properties intrinsic to animage being compressed. The approach can permit creation of a compressedimage that satisfies or guarantees a predetermined (e.g., required)degree of correlation with an original image prior to compression.Accordingly, the disclosed compression technique can depart fromdefinitions of image quality that rely on subjective metrics (e.g.,subjective quality indices that map into DCT quantization matrices), andcan permit defining image quality objectively, according to a restoredimage—obtained from decompression of the compressed image, forexample—and correlation thereof with the original image. For apredetermined degree of correlation between an original digital objectand a restored image associated therewith, in one aspect, thecompression technique can utilize the variation between values of pixelswithin the original image to determine dynamically a quantization matrixassociated with a space-domain-to-frequency-domain transform, whereinthe quantization matrix can permit compression of a digital object witha predetermined quality. In certain implementations, for example, todetermine a DCT quantization matrix (a quantization matrix associatedwith a DCT transform) for such predetermined degree of correlation, adigital variation metric of a digital object can be assessed via astandard deviation (σ) of pixel values contained in a data structurerepresentative of the digital object. In other implementations, forexample, a digital variation metric of the digital object can beassessed, for example, via a skewness of pixel values contained in thedata structure representative of the digital object. In one aspect, aspecific standard deviation value can be utilized as an argument in amathematical relationship that can determine a specific DTC quantizationmatrix. In certain embodiments, for example, such mathematicalrelationship can be a linear relationship that can be establishedthrough numerical experimentation comprising detailed analysis of aplurality of digital objects, such as a set of 730 24-bit RGB images,with standard deviation values ranging from about 25 to about 100.

In another aspect, certain embodiments can reveal, for example, a linearrelationship between image variation and DCT quantization matrices for aspecific desired level of correlation among an original digital objectand a restored digital object associated therewith. In one exemplaryimplementation, analysis of a standard set of 31 DCT quantizationmatrices and 730 24-bit bitmap images with variations ranging from 25 to100 can illustrate such linear relationship.

FIG. 1 illustrates a block diagram of an exemplary compression technique100 in accordance with one or more aspects of the disclosure. Variousimplementation features of the exemplary technique 100 can be achievedwith the exemplary system 200, shown in FIG. 2, for compression of adigital object. As illustrated, the compression technique 100 operateson an original digital object 110 and comprises four stages representedby functional blocks 120-150. Original digital object 110 can beembodied in a data structure representative of a digital asset, ordigital object, such as an image, a video segment, an audio segment, andso forth. In certain embodiments, the original digital object 110 can bea non-moving bitmap image having N_(x)×N_(y) pixels aligned in M×Mmacroblocks, each pixel having a D-bit of color depth, where N_(x),N_(y), M, and D are natural numbers. For example, in one embodiment,N_(x)=320, N_(y)=240, M=16, and D=24.

The original digital object 110 (e.g., an image) can be received atblock 120 in which a space-domain-to-frequency-domain transform (e.g., adiscrete cosine transform) can be applied to such object. As a result,the original digital object 110 can be transformed from a representationin space domain to a representation in frequency domain, thus generatinga transformed instance of the original digital object 110 that can beconveyed to block 130. Accordingly, block 120 is referred to asspace-to-frequency transformation 120. In one implementation in whichthe original digital object 110 comprises a plurality of M×Mmacroblocks, application of a discrete cosine transform to each one ofthe plurality of M×M macroblocks can be transformed into a plurality ofDCT coefficients. As an example, in embodiment 200, a compression engine210 can receive the original digital object 110, and a transformationcomponent 212 can transform data in each macroblock of M×M macroblocksof the original digital object 110 into a plurality of coefficientsaccording to the space-domain-to-frequency-domain transform. In oneimplementation, such coefficients can be real numbers.

As illustrated, the transformed instance of the original digital object110 can be quantized at block 130 (referred to as quantization block130). To at least such end, in one aspect, a plurality of coefficients(e.g., DCT coefficients) representing a macroblock of a transformedinstance of the original digital object 110 can be arithmetically ANDedwith a quantization matrix. Quantization can result in removal ofcertain high-frequency component(s) from a digital representation of thetransformed instance of the original digital object 110, and retentionof other frequency component(s) from such representation. As describedherein, the one or more frequencies that can be retained can befrequencies that are most perceptible to the human visual system. In animplementation in which the space-domain-to-frequency-domain transformis a two-dimensional (2D) direct cosine transform (DCT), eachquantization matrix utilized at block 130 can be referred to as a DCTquantization matrix and, in one aspect, each DCT quantization matrix bea 16×16 matrix. The number of non-zero matrix elements in a DCTquantization matrix represents a number of frequency components retainedin such matrix. As an example, in embodiment 200, a quantizationcomponent 216 can perform an arithmetic AND operation between a set ofM×M coefficients according to the space-domain-to-frequency-domaintransform and an M×M quantization matrix. In one implementation in whichthe original digital object 110 is a digital image having N_(x)×N_(y)pixels aligned in M×M macroblocks, the quantization component 216 canperform (N_(x)×N_(y))/(M×M) arithmetic AND operations between respectivesets of M×M coefficients according to thespace-domain-to-frequency-domain transform and a single M×M quantizationmatrix.

The original digital object 110 also can be received at block 140(referred to as analysis block 140) for analysis. In one aspect, suchobject can be analyzed prior to implementation of quantization block 130and, as an outcome of the analysis, a specific quantization matrixassociated with a space-to-frequency transformation can be selected forquantization, at block 130, of the transformed instance of the originaldigital object 110. In certain implementations, the specificquantization matrix can be conveyed to the quantization block 130 to beutilized in quantization of the transformed instance of the originaldigital object 110. As an example, in embodiment 200, a qualityassurance unit 230 can include an analysis component 232 that performsthe analysis of a data structure indicative of the original digitalobject 110. The quality assurance unit 230 also can comprise a maskconstructor component 234, functionally coupled to the analysiscomponent 232, that can supply (e.g., acquire, acquire and transmit,generate, generated and transmit, update, update and transmit, or thelike) a quantization matrix in response to an outcome of the analysisperformed by the analysis component 232.

At block 150, a quantized representation of a transformed instance ofthe original digital object 110 can be encoded in order to increasecompression efficiency. One or more types of encoding, such as entropyencoding, run-length encoding, Huffman encoding, or the like, can beimplemented in the exemplary compression technique 100. Block 150 isreferred to as encoding block 150. Output of encoding block 150 is acompressed digital object 160 (a non-motion picture), which can beembodied in another data structure representative of the digital assetassociated with the original digital object. As an example, inembodiment 200, the compression engine 210 can comprise an encodercomponent 218 (also referred to as encoder 218) that can encode thetransformed instance of the original digital object 110. Thetransformation component 212, the quantization component 216, andencoder 218 can be functionally coupled, and exchange information (e.g.,computer-executable instructions, data, metadata, and/or signaling), viaa bus 217.

In the exemplary compression technique 100, the compressed digitalobject 160 can be conveyed to analysis block 140 for analysis. In oneimplementation, for example, the compressed digital object 160 can beanalyzed to determine fidelity of the compressed digital object 160 withrespect to the original digital object 110 image yielding the compressedimage in response to compression. Fidelity of the compressed image canbe indicative of image quality. In one aspect, determining such fidelitycan comprise determining if compression quality satisfies apredetermined quality criterion. In one aspect, the compression qualityof the compressed digital object 160 can be determined by the degree ofcorrelation between the original digital object 110 and a restoreddigital object obtained from decompressing the compressed digital object160. The degree of correlation is a measure of the statisticalrelationship among a first group {X} of pixel values representative ofthe original digital object 110 and a second group {Y} of pixel valuesrepresentative of the restored digital object. In certainimplementations, the degree of correlation is determined by the Pearsonproduct-moment correlation coefficient (or Person's r). For example,such correlation coefficient (ρ) can be computed as the ratio betweenthe covariance of {X} and {Y} (cov(X,Y)) and the product of the standarddeviations σ_(x) and σ_(y) of {X} and {Y}:

$\rho = {\frac{{cov}\left( {X,Y} \right)}{\sigma_{X}\sigma_{Y}}.}$

It should be appreciated that other metrics suitable to establish thedegree of correlation, such as Person's χ² test, also are contemplatedin the disclosure. In general, a higher correlation coefficient canconvey that a restored instance of the original digital object 110 canhave higher fidelity with respect to the original digital object 110. Inone implementation of the disclosed approach, an end-user device canspecify a quality criterion (QC) 144 that can establish an extent towhich an end-user associated with the end-user device can require thecompressed image to correlate with the original image, e.g., to specifythe correlation coefficient. As an example, in embodiment 200, theanalysis component 232 can determine the degree of correlation betweenthe original object 110 and the restored digital object obtained fromdecompressing the compressed digital object 160, which can be receivedat the quality assurance unit 230 from the compression engine 210.Analysis component 232 can access a set of one or more quality criteria252 to compare such degree of correlation with at least one of the oneor more quality criteria 252.

To determine the compression quality of the compressed digital object160, in one aspect, the analysis block 140 can perform decompression. Toat least such end, in the exemplary compression technique 100, theanalysis block 140 can implement (e.g., perform or cause to perform)three blocks: (1) Decoding block 170, in which the compressed digitalobject 160 is decoded in accordance with the inverse process of theencoding process implemented at encoding block 150. For example, in ascenario in which the encoding process is Huffman encoding, the inverseprocess is Huffman decoding. A restored quantized digital object isgenerated as a result of decoding at block 170; (2) identity block 180,in which the restored quantized digital object is retained, e.g., theidentity operator is applied to such object; and (3) frequency-to-spacetransformation block 190, in which the restored quantized digital objectis transformed into a space representation through application of thefrequency-domain-to-spatial-domain transform that is the inversetransform of that utilized at block 120. As a result, a restoredinstance of the original digital object 110 can be generated. As anexample, in embodiment 200, the analysis component 232 can configurecompression engine 210, and components therein, to implement theforegoing blocks (1) through (3) and, as a result, generate the restoredinstance of the original digital object 110. To configure thecompression engine in such a manner of operation, the analysis component232 can transmit signaling to the compression engine 210 that configuresthe encoder 218 to operate as a decoder, the quantization component 216to operate as an identity operator, and the transformation component 212to perform frequency-domain-to-space-domain transform that is theinverse of the transform applied to the original digital object 110.

In one scenario in which the space-domain-to-frequency-domain transformis a DCT, the inverse DCT can be applied to a plurality of DCTcoefficients associated with the restored quantized digital object tocreate 3D RGB matrices indicative of the restored instance of theoriginal digital object 110. Analysis block 140 can generate a qualitymetric, such as a covariance between values of pixels indicative of theoriginal digital object 110 and values of pixels indicative of therestored instance of the original digital object 110. As anillustration, in embodiment 200, the analysis component 232 can generatethe quality metric.

Based on an outcome of the analysis, in one aspect, the compresseddigital object 160 can be assessed as satisfactory and, therefore,compression can terminate. For example, a quality metric determined(e.g., calculated) for a restored instance of the original digitalobject 110 obtained from the compressed digital object 160 can fulfill aquality criterion (e.g., a specific one of the one or more qualitycriteria 252), such as a specific threshold for quality metrics, thusleading to termination of a compression cycle (e.g., compression andanalysis). In such scenario, in embodiment 200, a compressed digitalobject 224 can be supplied by the compression engine 210. The compresseddigital object 224 can be the compressed digital object 160. In anotheraspect, a different outcome of the analysis can indicate thatcompression is to be re-effected with an alternative quantization matrixhaving, for example, a larger number of non-zero matrix elements.Collectively, such matrix elements can determine a number ofhigh-frequency components retained in the described compressiontechnique. In one scenario, in embodiment 200, the analysis component232 can signal the compression engine 210 that the original digitalobject 110 is to be compressed with the alternative quantization matrix,and can signal the mask constructor 234 to supply the alternativequantization matrix. In another scenario, in embodiment 200, theanalysis component 232 can signal the compression engine 210 that theoriginal digital object 110 is to be compressed with the alternativequantization matrix, and can select the alternative quantization matrixfrom a set of one or more quantization matrices retained in memory 250.For another example, another quality metric associated with thecompressed digital object 160 may not fulfill the quality criterion. Insuch scenario, the alternative quantization matrix can be determined(e.g., selected) at the analysis block 140. In addition, the transformedinstance of the original digital object 110 can be quantized at block140 in accordance with the alternative quantization matrix, and theresulting alternative quantized representation of the transformedinstance of the original digital object 110 can be encoded at theencoding block 150. An alternative compressed digital object can embodythe compressed digital object 160, which can be conveyed to the analysisblock 140 for further analysis.

It should be appreciated that the exemplary compression technique 100can determine dynamically a quantization matrix based on an intendedcompression quality, which can be determined, for example, by a qualitycriterion. As illustrated, memory 250 can comprise one or more memoryelements (quality criteria 252) having one or more predetermined (orpreconfigured) quality criteria. It should further be appreciated thatthe analysis performed in the exemplary technique 100 can be utilized togenerate information associated with quantization matrices that can besuitable to achieve a predetermined compression quality, as indicated bya predetermined correlation coefficient χ between a restored instance ofan original digital object, for a specific digital variation metric Σ ofthe original digital object.

A quantization matrix that produces, as part of implementation of acompression technique (e.g., exemplary compression technique 100) of thedisclosure, a compressed digital object having the predeterminedcompression quality can be deemed to be an optimal or nearly optimalquantization matrix. In the alternative, a quantization matrix that doesnot produce, indirectly, via implementation of a quantization block, acompressed digital object having the predetermined compression qualitycan be deemed sub-optimal. In certain implementations, the digitalvariation metric can be represented by the standard deviation between aplurality of digitized values (e.g., pixel values) associated with theoriginal digital object 110. Based on such information, a mappingbetween a pair (χ, Σ) and a specific quantization matrix

can be generated. In one aspect, generation of such mapping for aspecific value of c can comprise determining, e.g., calculating viaregression, a functional relationship among a quantization matrix and adigital variation metric. In another aspect, for a specific value of χ,the mapping (or the functional relationship) can be represented as alookup table relating values of Σ with quantization matrices

. In one aspect, the lookup table can have the image variation metricand the specific value χ as primary key.

As an example, quality assurance unit 230 can generate the informationassociated with quantization matrices that can be suitable to achieve apredetermined compression quality. To at least such end, in one aspect,the quality assurance unit 230 can receive a plurality of originaldigital objects having a wide range of digital variation metrics, andcan configure compression engine 210 to compress each one of theplurality of original digital objects for each one a plurality ofquantization matrices. In one aspect, mask constructor 234 can generatethe plurality of quantization matrices. Such compression can yield aplurality of compressed digital objects. In addition, the qualityassurance unit 230 can configure the compression engine 210 to restore(or decompress) each one of the plurality of compressed digital objects.In one aspect, analysis component 232 can determine the fidelity of eachof the restored digital objects and, for a specific correlationcoefficient χ, generate a set of one or more 2-tuples, each 2-tuplecomprising a digital variation metric for an original object and aparameter representative of the number of frequency components retainedin the quantization stage that yielded, during compression of theoriginal digital object, a compressed digital object having acompression quality determined substantially by the specific correlationcoefficient χ. In one implementation, analysis component 232 can performregression (linear or non-linear) to the set of one or more 2-tuples toextract a functional relationship among the digital variation metric andthe parameter representative of the number of frequency componentsretained in the quantization stage. Such functional relationship canembody or comprise the mapping for a pair (χ, Σ), and a specificquantization matrix

can be generated.

Generation of such mapping, in one aspect, can permit simplifying theexemplary compression technique 100 by removing the iterative featuresof the compression of an original digital object (e.g., an image) thatare associated with attaining a predetermined compression quality. FIG.3 illustrates a block diagram of an exemplary compression technique 300representative of a simplified version of the exemplary compressiontechnique 100 in accordance with one or more aspects described herein.Various implementation features of the exemplary technique 300 areillustrated with the exemplary system 200 for compression of a digitalobject.

In the exemplary compression technique 300, an original digital object310 can be transformed from a space-domain representation to afrequency-domain representation at block 120, as described herein. As anexample, in embodiment 200, transformation component 212 can generate aplurality of coefficients or other data indicative of a frequency-domainrepresentation of the original digital object 310. Prior toimplementation of quantization block 130, the original digital object310 can be analyzed at analysis block 320 to determine, for example, adigital variation metric Σ of such object. As described herein, analysiscomponent 232 can perform such analysis. In one aspect, the digitalvariation metric in conjunction with a correlation coefficient χ candetermine a specific lookup table for a quantization matrix forimplementation of quantization block 130. The correlation coefficient χcan be supplied, in certain scenarios, within a quality criterion (QC)324 that can be provided to the analysis block 320. In other scenarios,one or more predetermined quality criteria (e.g., one or several of theone or more quality criteria 252) and associated correlationcoefficient(s) can be retained in the analysis block 320. As an example,in embodiment 200, such correlation coefficient(s) can be retained inmemory 250 as part of quality criteria 252. Based at least on thecorrelation coefficient χ and associated lookup table, a specificquantization matrix

_(Σ;χ) can be selected at the analysis block 320 for quantization of thefrequency-domain representation of the original digital object 310. Inone scenario, for example, mask constructor 234 can select the aspecific quantization matrix

_(Σ;χ). In another scenario, for another example, analysis component 232can select such quantization matrix. Memory 250 can comprise one or morememory elements having at least one look-up table, the one or morememory elements are referred to as look-up table(s) 254.

A quantized instance of the frequency-domain representation of theoriginal digital object 310 can be encoded at encoding block 150 and acompressed digital object 330 can be supplied (e.g., generated, orgenerated and transmitted). It can be appreciated that compresseddigital object 330 can have a compression quality associated with thecorrelation coefficient χ in view of the selection of the specific

_(Σ;χ). As an illustration, encoder 218 can encode the quantizedinstance of the frequency-domain representation of the original digitalobject 310 (represented as compressed digital object 110 in FIG. 2.),and can output the compressed digital object 330 (represented ascompressed digital object 224 in FIG. 2.)

In the exemplary compression technique 300, selection of a dynamicquantization matrix can be dynamic, in response to a digital variationmetric of an original digital object to be compressed and/or to anintended compression quality represented by a quality criterion. In oneaspect, implementation cost (e.g., computational demand) for theexemplary compression technique 300 can be lower than such cost for theexemplary compression technique 100.

As described herein, the exemplary system 200 for compression of adigital object can operate in analysis mode, permitting generation ofinformation associated with relationships among quantization matricesand digital variation metrics, each of the relationship being specificto a compression quality metric. Various aspects of operation inanalysis mode and related analysis are described in greater detailbelow.

To perform the foregoing analysis across a range of digital variationmetrics, a set of N digital objects (e.g., images) can be accessed(e.g., generated, received, selected, or the like). In embodiment 200,analysis component 232 can receive such set of objects. In one aspect,the resolution of each digital object in such set can be normalized to320×240 pixels. While such normalization is not required, normalizingthe resolution of a digital object (or digital asset) can permit, atleast in part, aligning individual pixel blocks to macroblocks utilizedto represent the digital object. In scenarios in which resolutionnormalization of a digital object does not yield alignment of blockswith macroblocks, padding (addition of null data) can be applied to adata structure representing the digital object in order to improve orattain alignment. In another aspect, the normalized digital images canbe converted into 24-bit 320×240 red-green-blue (RGB) bitmap images, andeach image can be arranged in 16×16 macroblock-aligned 3-dimensional RGBmatrices. It should be appreciated that other pixel resolutions,macroblock sizes, and/or bit size of color depth (or value of D) can beutilized in the analysis. In one embodiment, for example, N can be equalto 730.

As described herein, in a compression technique, effectiveness ofquantization (DCT quantization or otherwise) can depend, at least inpart, on a number of frequency components retained in a quantizationmatrix (e.g., a DCT quantization matrix). In one embodiment, a set of TDCT quantization matrices can be utilized for the analysis performed atanalysis block 140. Here, T is a natural number which, in certainembodiments, for example, can be equal to 31. In embodiment 200, maskconstructor 234 can generate the set of T DCT quantization matrices. Asdescribed herein, each DCT quantization matrix, in one aspect, can be a16×16 matrix that can be indexed with an numeric index I ranging from 1through T (e.g., 31), each index representing a number of frequencycomponents retained in the a DCT quantization matrix associated withsuch index, with larger indices representing a larger number offrequency components. As an illustration, for I=1, the DCT quantizationmatrix comprises one low frequency component (e.g., the DC component);for I=2, the DCT quantization component can comprise three frequencycomponents; for I=3, the associated DCT quantization matrix can comprisesix frequency components; and so forth. In one aspect, the T DCTquantization matrices utilized to perform the analysis described hereincan be representative of DCT quantization matrices conventionallyutilized for JPEG image compression. The 16×16 DCT quantization matricesin the set of T (e.g., 31) DCT quantization matrices can be utilized tomask directly any high-frequency components in a DCT-transformedmacroblock associated with a digital object (e.g., original digitalobject 110). In embodiment 200, analysis component 232 can perform anarithmetic AND operation among a DCT-transformed macroblock associatedwith a digital object and a DCT quantization matrix. It should beappreciated that directly masking high-frequency components in aDCT-transformed macroblock permits a consistent approach to analyzing arelationship between digital variation metrics and DCT quantizationmatrices. As an example, Eq. (2) presents an exemplary quantizationmatrix for a 16×16 macroblock within a data structure representing adigital object.

$\begin{matrix}{\overset{\leftrightarrow}{Q} = \begin{bmatrix}1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\end{bmatrix}} & {{Eq}.\mspace{14mu} (2)}\end{matrix}$

Another exemplary quantization mask matrix suitable for a 16×16macroblock within the data structure representing the digital object canbe the following:

$\begin{matrix}{\overset{\leftrightarrow}{Q^{\prime}} = \begin{bmatrix}1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\end{bmatrix}} & {{Eq}.\mspace{14mu} (3)}\end{matrix}$

Yet another exemplary quantization mask matrix suitable for a 16×16macroblock within the data structure representing the digital object canbe the following:

$\begin{matrix}{\overset{\leftrightarrow}{Q^{''}} = \begin{bmatrix}1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\end{bmatrix}} & {{Eq}.\mspace{14mu} (4)}\end{matrix}$

Still another exemplary quantization mask matrix suitable for a 16×16macroblock within the data structure representing the digital object canbe the following:

$\begin{matrix}{\overset{\leftrightarrow}{Q^{''\prime}} = \begin{bmatrix}1 & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\end{bmatrix}} & {{Eq}.\mspace{14mu} (5)}\end{matrix}$

Application of a discrete cosine transform to each 16×16 macroblockwithin an original digital object (e.g., an original image (alsoreferred to as raw image)) can be transformed into a plurality of DCTcoefficients. In embodiment 200, transformation component 212 canperform the DCT transformation. As described herein, the resultingmacroblock containing the plurality of DCT coefficients can bearithmetically ANDed with each DCT quantization matrix of a set of N_(Q)DCT quantization matrices (e.g., the set of 31 DCT quantization matricesdescribed herein) to yield, respectively, N_(Q) sets (e.g., 31 sets) ofDCT coefficients associated with the macroblock. Here, N_(Q) is anatural number equal to or greater than 1. Such procedure can yieldN_(Q) (e.g., N_(Q)=31) different quantized digital objects (e.g.,quantized images); one quantized digital object for each instance of aDCT quantization matrix.

In one aspect, the original digital object (e.g., original image) can berestored by applying the inverse DCT to a quantized digital object(e.g., a quantized image) of the N_(Q) quantized digital objects. Inembodiment 200, as described herein, analysis component 232 can beconfigured to perform a frequency-domain-to-space-domain transform thatis the inverse of the space-domain-to-frequency-domain applied to anoriginal digital object. As described herein, in one aspect, the inverseDCT can be applied to a plurality of DCT coefficients associated withthe quantized digital object to create 3D RGB matrices indicative of arestored digital object representative of the original digital object.In one implementation, the inverse DCT can be applied to each one of theN_(Q) quantized digital objects; thus, the original digital object canbe associated with a set of N_(Q) restored digital objects, eachrestored digital object being associated with a specific DCTquantization matrix, which can be represented by a specific numericindex. As an example, when N=730 and N_(Q)=31, a set of 730×31=22661restored digital objects can be generated.

A correlation coefficient between data indicative of content of anoriginal digital object and data indicative of content of a restoreddigital object can be calculated, the restored digital object beingobtained from the original digital object after compression. Inembodiment 200, as described herein, analysis component 232 candetermine such correlation coefficient. In one implementation, suchcorrelation coefficient can be calculated for each one of a plurality ofrestored digital objects generated with a specific DCT quantizationmatrix utilized in a scheme for compression of a plurality of originaldigital objects, each restored digital object of such plurality beingrespectively associated with an original digital object of the pluralityof original digital objects. In one aspect, the correlation coefficientincreases with the number of frequency components present in the DCTquantization matrix. As an illustration, Table I presents exemplaryresults of a calculation of correlation coefficients between restoreddigital objects and associated original digital objects as obtained fora compression scheme that includes quantization matrices retaining anincreasing number of frequency components. In Table I, the number offrequency components is indicated with an numeric index, referred to asDCT quantization matrix index. The exemplary results exhibit a linearrelationship between the correlation coefficient and the DCTquantization mask index.

TABLE I Relationship between correlation coefficient and quantizationmatrix order for a specific standard deviation. DCT StandardQuantization Correlation Deviation Matrix Index Coefficient 26.296 155.250 26.296 2 61.578 26.296 3 64.634 26.296 4 66.404 26.296 5 68.08926.296 6 69.472 26.296 7 71.865 26.296 8 73.614 26.296 9 75.677 26.29610 77.835 26.296 11 79.424 26.296 12 80.886 26.296 13 82.231 26.296 1483.219 26.296 15 84.226 26.296 16 84.933 26.296 17 84.933 26.296 1885.450 26.296 19 85.813 26.296 20 86.053 26.296 21 86.236 26.296 2286.405 26.296 23 86.527 26.296 24 86.635 26.296 25 86.704 26.296 2686.767 26.296 27 86.811 26.296 28 86.841 26.296 29 86.862 26.296 3086.879 26.296 31 86.889

In one aspect, for DCT quantization matrices, results of the calculationof correlation coefficients among a restored digital object and anassociated original digital object can convey that a linear relationshipis present between digital object variations and the DCT quantizationmatrix indices, wherein a digital object variation can be embodied inthe standard deviation of a non-empty set of pixel values of an originaldigital object. In another aspect, a confidence level of about 95%,corresponding to a p-value of 0.05, can be utilized to reject the nullhypothesis and determined that the exemplary results exhibit a linearrelationship. In one embodiment, parameters of a linear equationindicative of the linear relationship between digital object variationand DCT quantization matrix index can be extracted through applicationof a linear regression to the exemplary results. In embodiment 200, forexample, analysis component 232 can perform a regression (linear ornon-linear) on a set of data and extract such parameters.

In another aspect, DCT quantization matrices retaining differentfrequency components can yield a specific correlation coefficient (χ)between a restored digital object and an associated original digitalobject. In addition, original digital objects having different digitalvariation metrics can exhibit the specific correlation coefficient for aplurality of DCT quantization matrices. Accordingly, a plurality of(digital variation metric, DCT quantization matrix index) 2-tuples canbe generated for the specific correlation coefficient. In oneimplementation in which the original digital objects are embodied indigital images and the digital variation metric is embodied in astandard deviation (σ) of pixel values of a digital image, an exemplaryset of 762 standard deviation values can be generated for χ=0.85. Forsuch set, 341 values can be unique values which define a sample size(n). In addition, for each of the 341 standard deviation values, a DCTquantization matrix yielding a restored digital image exhibiting χ=0.85can be generated. Accordingly, a set of 341 2-tuples (σ, I) can begenerated. FIG. 4 illustrates a rendering of the 341 2-tuples and alinear function 410 indicative of the relationship between DCTquantization matrix index and standard deviation. It can be appreciatedthat larger digital variations have associated smaller DCT quantizationmatrices indices. A linear regression applied to the 341 2-tuples yieldsthe linear function (represented with a straight grey line in FIG. 4)which can be indicated as I(σ)=b₁σ+b₀, with slope b₁=−0.1646 and theordinate-intercept (or Y intercept) b₀=18.8391. Results of the linearregression are illustrated in FIG. 5.

The linear function I(σ) permits generating a look-up table for a DCTquantization matrix index for a specific standard variation σ. Anexemplary look-up table for χ=0.85 is illustrated in Table II.

TABLE II Standard deviation and associated DCT quantization matrix indexfor a digital correlation coefficient χ = 0.85. DCT StandardQuantization Deviation Matrix Index 1 19 2 19 3 18 4 18 5 18 6 18 7 18 818 9 17 10 17 11 17 12 17 13 17 14 17 15 16 16 16 17 16 18 16 19 16 2016 21 15 22 15 23 15 24 15 25 15 26 15 27 14 28 14 29 14 30 14 31 14 3214 33 13 34 13 35 13 36 13 37 13 38 13 39 12 40 12 41 12 42 12 43 12 4412 45 11 46 11 47 11 48 11 49 11 50 11 51 10 52 10 53 10 54 10 55 10 5610 57 9 58 9 59 9 60 9 61 9 62 9 63 8 64 8 65 8 66 8 67 8 68 8 69 7 70 771 7 72 7 73 7 74 7 75 6 76 6 77 6 78 6 79 6 80 6 81 6 82 5 83 5 84 5 855 86 5 87 5 88 4 89 4 90 4 91 4 92 4 93 4 94 3 95 3 96 3 97 3 98 3 99 3

Generally, for a digital correlation coefficient χ, a functionI^((χ))(σ) relating to a quantization matrix index I and a standarddeviation σ can be obtained, for example, from a regression (eitherlinear or non-linear) applied to a plurality of Q(σ, I) tuples. TableIII illustrates exemplary parameters (y-intercept and slope) that definea linear function I^((χ))(σ) for 11 values of χ ranging from 80 to 90.The illustrated linear functions can be obtained via application of alinear regression to a plurality of (σ, I^((χ))) tuples for each valueof χ shown at Table III. Regression coefficients indicative ofsuitability of a linear relationship between I and also are presented,conveying that for larger values of χ, a linear relationship is a betterfit for a plurality of (σ, I^((χ))) tuples.

TABLE III Exemplary parameters defining a linear function I^((χ)) (σ)for different values of digital correlation coefficient χ. CorrelationRegression Coefficient Coefficient Y-intercept Slope 80 −0.178171813.20224 −0.09084 81 −0.2119922 14.27443 −0.10686 82 −0.140358 13.09206−0.07700 83 −0.2544053 16.17496 −0.13041 84 −0.2986262 17.85488 −0.1517985 −0.3152117 18.83910 −0.16455 86 −0.3617154 20.11851 −0.17832 87−0.3485397 20.28529 −0.17627 88 −0.3856367 22.77088 −0.20346 89−0.4111239 23.86839 −0.21432 90 −0.4168778 75.56278 −0.81879

It should be appreciated that exemplary compression techniques 100 and500 can be applied to in motion video compression, in which suchtechnique can be utilized to compress one or more types of frames, suchas I-frames and/or residual frames, including P frames and B frames.

FIGS. 6A-6B are high-level block diagrams of exemplary embodiments of acomputing device 600 in accordance with one or more aspects of thedisclosure. The computing device 602 can embody or can comprise one ormore of the quality assurance unit 230 or the compression engine 210. Inthe illustrated embodiments, the computing device 602 comprises a groupof one or more I/O interfaces 604, a group of one or more processors608, a memory 616, and a bus 612 that functionally couples (e.g.,communicatively couples) two or more of the functional elements of thecomputing device 602, including the group of one or more processors 608to the memory 616. In scenarios in which operation of computing device602 can be critical to network performance, the group of one or moreprocessors 608 can comprise a plurality of processors that can exploitconcurrent computing.

Functionality of the computing device 602 can be configured by a groupof computer-executable instructions (e.g., programming code instructionsor programming modules) that can be executed by at least one processorof the one or more processors 608. Generally, programming modules cancomprise computer code, routines, objects, components, data structures(e.g., metadata objects, data object, control objects), and so forth,that can be configured (e.g., coded or programmed) to perform aparticular action or implement particular abstract data types inresponse to execution by the at least one processor. For example, afirst group of computer-executable instructions can configure logicthat, in response to execution by the at least one processor, can enablethe computing device 602 to operate as a compression engine or qualityassurance unit, or both.

Data and computer-accessible instructions, e.g., computer-readableinstructions and computer-executable instructions, associated withspecific functionality of the computing device 602 can be retained inmemory 616. For instance, in the illustrated embodiment, memory elements618 a and 618 b comprise compression instruction(s) and analysisinstruction(s), respectively, and data storage 620 comprises qualitycriteria 252 and look-up table(s) 254. Such data and instructions canpermit implementation, at least in part, of the compression techniquethat can permit determining dynamically a satisfactory quantizationmatrix in accordance with one or more aspects of the disclosure. In oneaspect, the computer-accessible instructions can embody any number ofprogramming code instructions or program modules that permit specificfunctionality. In the subject specification and annexed drawings, memoryelements are illustrated as discrete blocks; however, such memoryelements and related computer-accessible instructions (e.g.,computer-readable and computer-executable instructions), and data canreside at various times in different storage elements (registers, memorypages, files, databases, memory addresses, buffers, etc.; not shown) inmemory 616.

As described herein, data storage 620 can comprise a variety of data,metadata, or both, associated with compression of a digital object andrelated dynamic generation of a quantization matrix in accordance withaspects described herein. As another illustration, in a configuration inwhich the computing device 602 can embody the quality assurance unit230, the data storage 620 comprise information associated with intendedquality of compression (e.g., quality criteria 252) and availableresources (e.g., look-up table(s) 254) available for dynamic generationof a quantization matrix in accordance with one or more aspects of thedisclosure.

Memory 616 also can comprise one or more computer-executableinstruction(s) for implementation of specific functionality of thecomputing device 602 in connection with the dynamic determination of aquantization matrix associated with a space-domain-to-frequency-domaintransform in a compression scheme in accordance with one or more aspectsof the disclosure. Such computer-executable instructions can be retainedas one or more memory elements. For example, in the illustratedembodiment, the memory 616 comprises a memory element labeledfunctionality instruction(s) 617, which can comprise memory elementslabeled compression instruction(s) 618 a and analysis instruction(s) 618b. In additional or alternative embodiments, the computing device 602can comprise one of memory elements 618 a or 618 b depending, forexample, on whether computing device 602 embodies or comprises thecompression engine 210 or the quality assurance unit 230. In one aspect,as described herein, compression instruction(s) 618 a and analysisinstructions(s) 618 b can be stored as an implementation (e.g., acompiled instance) of one or more computer-executable instructions thatimplement and thus provide at least the functionality of the methodsdescribed herein (e.g., exemplary methods 700-900 presented in FIGS.7-9). Compression instruction(s) 618 a and analysis instructions(s) 618b also can be transmitted across some form of computer readable media.

It should be appreciated that different functionality instruction(s) canrender physically alike computing devices, such as computing device 602,into functionally different components (e.g., a compression engine or aquality assurance unit), with functional differences dictated by logic(e.g., computer-executable instructions and data) specific to each oneof such computing devices and defined by the functionalityinstruction(s) 617. In an exemplary configuration in which the computingdevice 602 embodies the compression engine 210, the functionalityinstruction(s) 617 can include compression instruction(s) 618 a withoutincluding the analysis instruction(s) 618 b. In one aspect, thecompression instruction(s) 618 a can embody computer-accessibleinstructions that, in response to execution by a processor (e.g., aprocessor of the one or more processors 608), can permit the computingdevice 602 to compress a digital object (e.g., a digital image) inaccordance with one or more aspects described herein. In anotherexemplary configuration in which the computing device 602 embodies thequality assurance unit 230, the functionality instruction(s) 618 caninclude analysis instruction(s) 618 b without including the compressioninstruction(s) 618 a. In one aspect, the analysis instruction(s) 618 acan embody computer-accessible instructions that, in response toexecution by a processor, can permit the computing device 602 todetermine (e.g., generate, generate and transmit) dynamically aquantization matrix associated with a space-domain-to-frequency-domaintransform (DCT transform, wavelet transform, or the like) and/orgenerate a functional relationship (such as a linear relationship) amonga digital variation metric of a digital object to be compressed and aparameter indicative of a quantization matrix associated with thespace-domain-to-frequency-domain transform.

Memory 616 can be embodied in a variety of computer-readable media.Exemplary computer-readable media can be any available media that isaccessible by a processor in a computing device, such as one processorof the group of one or more processors 608, and comprises, for example,both volatile and non-volatile media, removable and non-removable media.As an example, computer-readable media can comprise “computer storagemedia,” or “computer-readable storage media,” and “communicationsmedia.” Such storage media can be non-transitory storage media.“Computer storage media” comprise volatile and non-volatile, removableand non-removable media implemented in any methods or technology forstorage of information such as computer readable instructions, datastructures, program modules, or other data. Exemplary computer storagemedia comprises, but is not limited to, RAM, ROM, EEPROM, flash memoryor other memory technology, CD-ROM, digital versatile disks (DVD) orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe utilized to store the desired information and which can be accessedby a computer or a processor therein or functionally coupled thereto.

The memory 616 can comprise computer-readable non-transitory storagemedia in the form of volatile memory, such as random access memory(RAM), electrically erasable programmable read-only memory (EEPROM), andthe like, or non-volatile memory such as read only memory (ROM). In oneaspect, memory 616 can be partitioned into a system memory that cancontain data and/or programming modules that enable essential operationand control of the computing device 602. Such program modules can beimplemented (e.g., compiled and stored) in memory element 622, referredto as operating system (OS) instruction(s) 622, and/or a portion ofmemory element 628, referred to as I/O interface(s) 628. The datacontained in the system memory can be system data that is retained inmemory element 624, referred to as system data storage 624. The OSinstruction(s) 622, the portion of the I/O interface(s) 628, and systemdata storage 624 can be immediately accessible to and/or are presentlyoperated on by at least one processor of the group of one or moreprocessors 608. The OS instruction(s) 622 can embody an operating systemfor the computing device. Specific implementation of such OS can dependin part on architectural complexity of the computing device 602. Highercomplexity affords higher-level OSs. Example operating systems caninclude Unix, Linux, iOS, Windows operating system, and substantiallyany operating system for a computing device. In certain scenarios, theoperating system embodied in OS instruction(s) 622 can have differentlevels of complexity based on particular configuration of the computingdevice 602.

Memory 616 can comprise other removable/non-removable,volatile/non-volatile computer-readable non-transitory storage media. Asan example, memory 616 can include a mass storage unit (not shown) whichcan provide non-volatile storage of computer code, computer readableinstructions, data structures, program modules, and other data for thecomputing device 602. A specific implementation of such mass storageunit (not shown) can depend on desired form factor of the computingdevice 602 and space available for deployment thereof. For suitable formfactors and sizes of the computing device 602, the mass storage unit(not shown) can be a hard disk, a removable magnetic disk, a removableoptical disk, magnetic cassettes or other magnetic storage devices,flash memory cards, CD-ROM, digital versatile disks (DVD) or otheroptical storage, random access memories (RAM), read only memories (ROM),electrically erasable programmable read-only memory (EEPROM), or thelike.

In certain embodiments, as illustrated in embodiment 650, the computingdevice 602 can comprise a functionality specific platform 654 that caninclude one or more components the permit functionality of the computingdevice 602. In one aspect, a component of the one or more components canbe a firmware component which can have dedicated resources (e.g., aprocessor, software, etc.) to implement certain functions that supportimplementation of or implement at least part of the functionality of thecomputing device 602 in accordance with one or more aspects of thedisclosure. In another aspect, the functionality specific platform 654can include at least a portion of the one or more processors 608 whichcan be dedicated to execution of a part or all of computer-accessibleinstructions contained in memory 616, in the block labeled asfunctionality instruction(s) 658, thus relieving at least some of thecomputational load from the one or more processors 608 for otheroperation of the computing device 602. In certain implementations, thecomputer-accessible instructions can comprise a portion of thecomputer-accessible instructions in compression instruction(s) 618 aand/or analysis instruction(s) 618 b. In one exemplary configuration inwhich the computing device 602 is configured to carry out compression ofdigital objects according to one or more aspects of the disclosure, thefunctionality specific platform 654 can be embodied in or can comprisethe compression engine 210 and functional element therein. In anotherexemplary configuration in which the computing device 602 is configuredto analyze data obtained from compression of digital objects and/or togenerate a quantization matrix dynamically based at least on features ofa compressed digital object and a desired quality of compression, thefunctionality specific platform 610 can be embodied in or can comprisethe quality assurance unit 230.

As described herein, features of compression of a digital object andrelated dynamic generation of a quantization matrix in accordance withone or more aspects of the disclosure, can be performed, at least inpart, in response to execution of software components by a processor.The software components can include one or more implementations (e.g.,encoding) of functionality instruction(s) 617, including compressioninstruction(s) 618 a and/or analysis instruction(s) 618 b.

In general, a processor of the group of one or more processors 608 canrefer to any computing processing unit or processing device comprising asingle-core processor, a single-core processor with software multithreadexecution capability, multi-core processors, multi-core processors withsoftware multithread execution capability, multi-core processors withhardware multithread technology, parallel platforms, and parallelplatforms with distributed shared memory (e.g., a cache). In addition orin the alternative, a processor of the group of one or more processors608 can refer to an integrated circuit with dedicated functionality,such as an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components, or any combination thereof designedto perform the functions described herein. In one aspect, processorsreferred to herein can exploit nano-scale architectures such as,molecular and quantum-dot based transistors, switches and gates, inorder to optimize space usage (e.g., improve form factor) or enhanceperformance of the computing devices that can implement the variousaspects of the disclosure. In another aspect, the one or more processors608 can be implemented as a combination of computing processing units.

The one or more input/output (I/O) interfaces 604 can functionallycouple (e.g., communicatively couple) computing device 602 to anotherfunctional element (component, unit, server, gateway node, repository,etc.) of system(s) or device(s) that can perform compression of digitalobjects, for example. Functionality of the computing device 602 that isassociated with data I/O or signaling I/O can be accomplished inresponse to execution, by a processor of the group of one or moreprocessors 608, of at least one I/O interface retained in memory element628. Such memory element is represented by the block I/O interface(s)628. In some embodiments, the at least one I/O interface embodies an APIthat permit exchange of data or signaling, or both, via an I/O interfaceof I/O interface(s) 604. In certain embodiments, the one or more I/Ointerfaces 604 can include at least one port that can permit connectionof the computing device 602 to other functional elements of theexemplary network environment 100. In one or more scenarios, the atleast one port can comprise network adaptor(s) such as those present inreference links, and other computing devices. In other scenarios, the atleast one port can include one or more of a parallel port (e.g., GPIB,IEEE-1284), a serial port (e.g., RS-232, universal serial bus (USB),FireWire or IEEE-1394), an Ethernet port, a V.35 port, or the like. Theat least one I/O interface of the one or more I/O interfaces 604 canenable delivery of output (e.g., output data, output signaling) to suchfunctional elements. Such output can represent an outcome or a specificaction of one or more actions described herein, such as in the methodsof FIGS. 7-9.

Bus 612, and the various configurations thereof, such as bus 612 and bus712, represents one or more of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. As an example, such architectures cancomprise an Industry Standard Architecture (ISA) bus, a Micro ChannelArchitecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video ElectronicsStandards Association (VESA) local bus, an Accelerated Graphics Port(AGP) bus, and a Peripheral Component Interconnects (PCI), a PCI-Expressbus, a Personal Computer Memory Card Industry Association (PCMCIA),Universal Serial Bus (USB), and the like.

In exemplary embodiments 600 and 650, the computer-executableinstructions stored in memory 616 can configure a processor of theprocessor(s) 608 to carry out various functions. In one aspect, theprocessor can be configured to determine an image variation metric of adigital image and to provide, based at least on the image variationmetric, a space-domain-to-frequency-domain transform quantization matrixfor compression of the digital image. In another aspect, the processorcan be further configured, by the computer-executable instructions, tocompute an image variation metric comprising one or more of a skewnessof values of a plurality of pixels contained in the digital image or astandard deviation of said values. In yet another aspect, the processorcan be further configured, by the computer-executable instructions, tocompute an image variation metric comprising a standard deviation ofvalues of a plurality of pixels contained in the digital image.

In still another aspect, the processor can be further configured, by thecomputer-executable instructions, to determine a two-dimensional (2D)discrete cosine transform (DCT) quantization matrix. In oneimplementation, to determine such quantization matrix, the processor canbe further configured, by the computer-executable instructions, tocompute a value of a predetermined linear function of the imagevariation metric, the value being indicative of the number of frequencycoefficients retained in the 2D DCT quantization matrix, and wherein thepredetermined linear function depends at least on a specific degree ofcorrelation between an original digital image and a restored imageobtained from restoring a compressed instance of the original digitalimage. The degree of correlation can determined by a Pearsonproduct-moment correlation coefficient computed in accordance with oneor more aspects described herein. In addition or in the alternative, theprocessor can be further configured, by the computer-executableinstructions, to select the 2D DCT quantization matrix from a lookuptable having the image variation metric and a correlation metricindicative of the specific degree of correlation as primary key, andwherein the lookup table comprises at least one value of a predeterminedlinear function of the image variation metric, the value beingindicative of a number of frequencies retained in the 2D DCTquantization matrix. In yet other implementations, the processor can befurther configured, by the computer-executable instructions, to acquirea specific correlation metric prior to determining the image variationmetric of the digital image.

In embodiments in which the computing device 602 comprises analysisinstruction(s) 618 b, the computing device 602, can carry out analysisof compression information associated with compression of a plurality ofdigital objects in response to execution of the analysis instruction(s)618 b by a processor of the processor(s) 608. Such analysis can permitgenerating a functional relationship between an original digital objectand a restored instance of such object obtained after compression of theoriginal digital object. In one aspect, implementing the analysis caninclude implementing the exemplary method 900 presented in FIG. 9 anddescribed herein. In addition or in the alternative, in embodiments inwhich the computing device 602 comprises the quality assurance unit 230as part of the functionality specific platform 654, operation of suchplatform can implement the analysis of compression informationassociated with compression of a plurality of digital objects inaccordance with the various aspects described herein.

In view of the various aspects of compression of digital objects, suchas those described herein, exemplary methods that can be implemented inaccordance with the disclosure can be better appreciated with referenceto the exemplary flowcharts in FIGS. 7-9. For simplicity of explanation,the exemplary methods disclosed herein are presented and described as aseries of actions (also referred to as steps), pictorially representedwith a block. However, it is to be understood and appreciated thatimplementation, and related advantages, of such methods is not limitedby the order of actions, as some actions may occur in different ordersand/or concurrently with other actions from that shown and describedherein. For example, the various methods (also referred to as processes)of the disclosure can be alternatively represented as a series ofinterrelated states or events, such as in a state diagram. Moreover,when disparate functional elements (network nodes, units, etc.)implement different portions of the methods of the disclosure, aninteraction diagram or a call flow can represent such methods orprocesses. Furthermore, not all illustrated actions may be required toimplement a method in accordance with the subject disclosure.

The methods disclosed throughout the subject specification and annexeddrawings can be stored on an article of manufacture, orcomputer-readable storage medium, to facilitate transporting andtransferring such methods to computing devices (e.g., desktop computers,mobile computers, mobile telephones, and the like) for execution, andthus implementation, by a processor or for storage in a memory.

FIG. 7 presents a flowchart of an exemplary method 700 for selecting aquantization mask suitable for compression of a digital content inaccordance with one or more aspects of the disclosure. In one aspect,one or more blocks of the subject example method 700 can be implemented(e.g., executed) by a computing device (e.g., computing device 602) or aprocessor integrated with or functionally coupled to such device. Incertain implementations, the computing device can embody one or more ofthe compression engine 210 or the quality assurance unit 230 orcomponent(s) therein (e.g., quantization component 216, analysiscomponent 232, mask constructor 232, a combination thereof, or thelike). At block 710 a digital variation metric of a digital object canbe determined. Block 710 is referred to as a determining action and, inone aspect, can comprise computing a digital variation metric. Asdescribed herein, the digital variation metric can be at least one of askewness of values of a plurality of pixels contained in the digitalobject or a standard deviation of such values. At block 720, aquantization matrix for compression of the digital object can beprovided based at least on the digital variation metric. Thequantization matrix can be associated with aspace-domain-to-frequency-domain transform, such as a DCT. Block 720 isreferred to as a providing action. In one aspect, the providing actioncan comprise determining a 2D DCT quantization matrix. As describedherein, in certain embodiments, determining the 2D DCT can comprisecomputing a value of a predetermined linear function of the digitalvariation metric. The value can be indicative of the number of frequencycoefficients (or frequency components) retained in the 2D DCTquantization matrix. The predetermined linear function can depend, forexample, at least on a specific degree of correlation between anoriginal digital object and a restored digital object (e.g., a restoredimage) obtained from restoring a compressed instance of the originaldigital object (e.g., a compressed image). In one alternative oradditional embodiment, the exemplary method 700 can comprise acquiringthe specific degree of correlation prior to the determining action.

In other embodiments, determining the 2D DCT quantization matrix cancomprise selecting the 2D DCT quantization matrix from a lookup tablehaving the digital variation metric and the specific degree ofcorrelation as primary key. The lookup table can comprise at least onevalue of a predetermined linear function of the digital variationmetric, the value being indicative of a number of frequency componentsretained in the 2D DCT quantization matrix.

FIG. 8 presents a flowchart of an exemplary method 800 for dynamicallyselecting a quantization matrix suitable for compression of a digitalcontent at a predetermined quality in accordance with one or moreaspects of the disclosure. In one aspect, one or more blocks of thesubject example method 800 can be implemented (e.g., executed) by acomputing device (e.g., computing device 602) or a processor integratedwith or functionally coupled to such device. In certain implementations,the computing device can embody one or more of the compression engine210 or the quality assurance unit 230 or component(s) therein (e.g.,quantization component 216, analysis component 232, mask constructor232, a combination thereof, or the like). At block 810, a correlationmetric associated with a predetermined digital object quality isacquired (e.g., accessed or received). In one aspect, the correlationmetric is indicative of an intended or desired degree of correlationamong an original digital object and a restored digital object obtainedby restoring a compressed instance of the original digital object. Atblock 820 a digital object is acquired. At block 830, a digitalvariation metric of a digital object is determined. As described herein,the digital variation metric can be at least one of a skewness of valuesof a plurality of pixels contained in the digital object or a standarddeviation of such values. At block 840, a quantization matrix forcompression of the digital object is provided based at least on thedigital variation metric and the correlation metric. The quantizationmatrix can be associated with a space-domain-to-frequency-domaintransform, such as a DCT. Aspects of implementation of block 840 can besubstantially the same as aspects of implementation of block 720 asdescribed herein. It should be appreciated that the quantization matrixthat can be provided is specific to the predetermined digital objectquality.

At block 850 it is determined if compression of digital objects is to becontinued. In the negative case, flow of the exemplary method 800terminates. In the alternative, flow is directed to block 860, in whichthe digital object is compressed according to a compression schemecomprising the quantization matrix. At block 870, it is determined ifthe predetermined digital object quality is to be updated. In theaffirmative case, flow is directed to block 810. Alternatively, flow isdirected to block 820, in which another digital object can be acquired.

As described herein, the quantization matrices provided in blocks 720and 840 include evaluation of a predetermined linear function. In otherembodiments, providing such quantization matrices can compriseevaluating a functional relationship, other than a linear relationship,among a digital variation metric of an object to be compressed and aparameter (e.g., a real number) indicative of a quantization matrix. Thefunctional relationship can be specific to a desired quality of adigital object restored after compression of an original digital object(e.g., a raw image).

FIG. 9 presents a flowchart of an exemplary method 900 for generatingthe foregoing functional relationship. In one aspect, one or more blocksof the subject example method 900 can be implemented (e.g., executed) bya computing device (e.g., computing device 602) or a processorintegrated with or functionally coupled to such device. In certainimplementations, the computing device can embody the quality assuranceunit 230 or component(s) therein (e.g., analysis component 232 or maskconstructor 232, or a combination thereof). At block 910, a digitalvariation metric for each one of a plurality of digital objects (e.g.,digital images, digital audio segments, a combination thereof, or thelike) is determined, thereby generating a first plurality of digitalvariation metrics. In one aspect, determining such digital variationmetric can comprise computing one or more of a standard deviation ofvalues of a plurality of pixels contained in each one of the firstplurality of digital objects, or a skewness of such values. At block920, each one of the plurality of digital objects is quantized accordingto a first plurality of quantization matrices, thereby generating aplurality of quantized digital objects. In one aspect, the plurality ofquantized digital objects can be respectively associated with theplurality of digital objects. Each quantization matrix of the firstplurality of quantization matrices is specific to aspace-domain-to-frequency-domain transform utilized in a compressionscheme comprising the quantization of each one of the plurality ofdigital objects. At block 930, each one of the plurality of quantizeddigital objects is restored, thereby generating a plurality of restoreddigital objects. In one aspect, the plurality of restored digitalobjects can be respectively associated with the plurality of digitalobjects. In one aspect, restoring a quantized digital object can beimplemented in accordance with aspects described herein in connectionwith FIG. 1, for example. At block 940, a plurality of correlationmetrics associated with the plurality of digital objects is generated.Each correlation metric of the plurality of correlation metrics canindicate a measure of the statistical relationship among a first groupof pixel values representative of each digital object of the pluralityof digital objects and a second group of pixel values representative ofa respective restored digital object. At block 950, a second pluralityof quantization matrices is selected from the first plurality ofquantization matrices, the second plurality of quantization matricesyielding a specified correlation metric of the plurality of correlationmetrics. At block 960, a second plurality of digital variation metricsis selected from the first plurality of digital variation metrics, thesecond plurality of digital variation metrics being respectivelyassociated with a second plurality of digital objects of the firstplurality of digital objects. In one aspect, each one of the secondplurality of digital images can exhibit the specified correlation metricin response to quantization according to each of the second plurality ofquantization matrices. At block 970, a functional relationship among thesecond plurality of digital variation metrics and a plurality of indicesrespectively associated with the second plurality of quantizationmatrices is determined. The functional relationship can be determined inaccordance with one or more aspects described herein. In one aspect,each one of the plurality of indices can convey a number of frequenciesretained in a respective quantization matrix.

When compared with conventional technologies for compression of digitalobjects, various advantages of the disclosure over such technologiesemerge from the subject specification. For example, the disclosure canprovide, dynamically, a quantization matrix suitable to achieve aspecific compression quality for compression of a digital object.

One or more embodiments of the subject disclosure can employ artificialintelligence (AI) techniques such as machine learning and iterativelearning. Examples of such techniques include, but are not limited to,expert systems, case based reasoning, Bayesian networks, behavior basedAI, neural networks, fuzzy systems, evolutionary computation (e.g.genetic algorithms), swarm intelligence (e.g. ant algorithms), andhybrid intelligent systems (e.g. expert inference rules generatedthrough a neural network or production rules from statistical learning).

While the systems, apparatuses, and methods have been described inconnection with exemplary embodiments and specific examples, it is notintended that the scope be limited to the particular embodiments setforth, as the embodiments herein are intended in all respects to beillustrative rather than restrictive.

Unless otherwise expressly stated, it is in no way intended that anyprotocol, procedure, process, or method set forth herein be construed asrequiring that its acts or steps be performed in a specific order.Accordingly, in the subject specification, where a description of aprotocol, procedure, process, or method does not actually recite anorder to be followed by its acts or steps or it is not otherwisespecifically stated in the claims or descriptions that the steps are tobe limited to a specific order, it is no way intended that an order beinferred, in any respect. This holds for any possible non-express basisfor interpretation, including: matters of logic with respect toarrangement of steps or operational flow; plain meaning derived fromgrammatical organization or punctuation; the number or type ofembodiments described in the specification or annexed drawings, or thelike.

It will be apparent that various modifications and variations can bemade without departing from the scope or spirit of the subjectdisclosure. Other embodiments will be apparent from consideration of thespecification and practice disclosed herein. It is intended that thespecification and examples be considered as non-limiting illustrationsonly, with a true scope and spirit of the subject disclosure beingindicated by the following claims.

What is claimed is:
 1. A method, comprising: determining, at a computingdevice, an image variation metric of a digital image; and providing, bythe computing device, based at least on the image variation metric, aspace-domain-to-frequency-domain transform quantization matrix forcompression of the digital image.
 2. The method of claim 1, wherein thedetermining step comprises computing an image variation metriccomprising a skewness of values of a plurality of pixels contained inthe digital image or a standard deviation of said values.
 3. The methodof claim 1, wherein the determining step comprises computing an imagevariation metric comprising a standard deviation of values of aplurality of pixels contained in the digital image.
 4. The method ofclaim 1, wherein the providing step comprises determining atwo-dimensional (2D) discrete cosine transform (DCT) quantizationmatrix.
 5. The method of claim 4, wherein determining the 2D DCTquantization matrix comprises computing a value of a predeterminedlinear function of the image variation metric, the value beingindicative of the number of frequency coefficients retained in the 2DDCT quantization matrix, and wherein the predetermined linear functiondepends at least on a specific correlation metric between an originaldigital image and a restored image obtained from restoring a compressedinstance of the original digital image.
 6. The method of claim 4,wherein determining the 2D DCT quantization matrix comprises selectingthe 2D DCT quantization matrix from a lookup table having the imagevariation metric and the specific correlation metric as primary key, andwherein the lookup table comprises at least one value of a predeterminedlinear function of the image variation metric, the value beingindicative of a number of frequencies retained in the 2D DCTquantization matrix.
 7. The method of claim 4, further comprisingacquiring a specific correlation metric prior to determining the imagevariation metric of the digital image.
 8. The method of claim 7, whereinthe acquiring step comprises acquiring data indicative of a Pearsonproduct-moment correlation coefficient.
 9. A method, comprising:determining an image variation metric for each one of a first pluralityof digital images, the determining step yielding a first plurality ofimage variation metrics; quantizing each one of the plurality of digitalimages according to a first plurality of quantization matrices for imagecompression, the quantizing step yielding a plurality of quantizedimages respectively associated with the plurality of digital images;restoring each one of the plurality of quantized images, the restoringstep yielding a plurality of restored images respectively associatedwith the plurality of digital images; determining a correlationcoefficient among a digital image and a respective restored image foreach one of the plurality of digital images; selecting from the firstplurality of quantization matrices for image compression a secondplurality of quantization matrices yielding a specified correlationcoefficient; selecting from the first plurality of image variationmetrics a second plurality of image variation metrics respectivelyassociated with a second plurality of digital images of the firstplurality of digital images, each one of the second plurality of digitalimages exhibiting the specified correlation coefficient in response toquantization according to each of the second plurality of quantizationmatrices; and determining a functional relationship among the secondplurality of image variation metrics and a plurality of indicesrespectively associated with the second plurality of quantizationmatrices, each one of the plurality of indices conveying a number offrequencies retained in a respective quantization matrix.
 10. The methodof claim 9, wherein determining the functional relationship comprisescomputing a linear regression among the second plurality of imagevariation metrics and a plurality of indices respectively associatedwith the second plurality of quantization matrices.
 11. The method ofclaim 9, further comprising composing a lookup table relating an imagevariation metric of the second plurality of variation metrics to a valueof the functional relationship, the value indicative of an index of theplurality of indices.
 12. The method of claim 9, wherein determining theimage variation metric comprises computing a standard deviation ofvalues of a plurality of pixels contained in each one of the firstplurality of digital objects.
 13. A device, comprising: a memory havingdata and computer-executable instructions stored thereon; and aprocessor functionally coupled to the memory and configured, by thecomputer-executable instructions, to determine an image variation metricof a digital image; and to provide, based at least on the imagevariation metric, a space-domain-to-frequency-domain transformquantization matrix for compression of the digital image.
 14. The deviceof claim 13, wherein the processor is further configured, by thecomputer-executable instructions, to compute an image variation metriccomprising one or more of a skewness of values of a plurality of pixelscontained in the digital image or a standard deviation of said values.15. The device of claim 13, wherein the processor is further configured,by the computer-executable instructions, to compute an image variationmetric comprising a standard deviation of values of a plurality ofpixels contained in the digital image.
 16. The device of claim 13,wherein the processor is further configured, by the computer-executableinstructions, to determine a two-dimensional (2D) discrete cosinetransform (DCT) quantization matrix.
 17. The device of claim 16, whereinthe processor is further configured, by the computer-executableinstructions, to compute a value of a predetermined linear function ofthe image variation metric, the value being indicative of the number offrequency coefficients retained in the 2D DCT quantization matrix, andwherein the predetermined linear function depends at least on a specificdegree of correlation between an original digital image and a restoredimage obtained from restoring a compressed instance of the originaldigital image.
 18. The device of claim 17, wherein the degree ofcorrelation is determined by a Pearson product-moment correlationcoefficient.
 19. The device of claim 16, wherein the processor isfurther configured, by the computer-executable instructions, to selectthe 2D DCT quantization matrix from a lookup table having the imagevariation metric and a correlation metric indicative of the specificdegree of correlation as primary key, and wherein the lookup tablecomprises at least one value of a predetermined linear function of theimage variation metric, the value being indicative of a number offrequencies retained in the 2D DCT quantization matrix.
 20. The deviceof claim 16, wherein the processor is further configured, by thecomputer-executable instructions, to acquire a specific correlationmetric prior to determining the image variation metric of the digitalimage.